Cogent AI
Cogent AI combines NLP (Natural Language Processing) to extract diagnosis information, chronic conditions, and Z codes from unstructured notes, ML/DL (Machine & Deep Learning) to improve the accuracy of HCC medical coding through adaptive algorithms, and OCR (Optical Character Recognition) to digitize scanned clinical documents, enabling seamless ICD-10 HCC mapping, RAF score calculation, and compliance with CMS risk adjustment coding guidelines.
This offers the healthcare organization the opportunity to boost HCC coding accuracy, reduce undercoding risk, and guarantee perfect reimbursement. Whether it’s urgent care medical coding or long-term chronic condition tracking, Cogent AI provides the accuracy required for scalable HCC risk adjustment.
With deep learning algorithms and real-time auditing, Cogent AI uses deep learning algorithms and real-time auditing to find undocumented HCC diagnosis codes, validate risk adjustment factors, and enforce HCC coding guidelines.
Cogent AI
HCC coding
Hierarchical Condition Category (HCC) coding is vital for risk adjustment and reimbursement in value-based care models. Cogent AI automates HCC coding by analyzing patient records and ensuring proper capture of chronic conditions.
Key Features of Cogent AI in HCC Medical Coding Automation
AI-powered HCC Medical Coding
Automatically extracts chronic conditions, Z codes, and MEAT criteria from unstructured EHR notes to assign accurate HCC diagnosis codes.
Real-Time RAF Score Calculator
Improves accuracy and compliance with insurance by calculating the RAF score based on the HHS risk model, which considers age, gender, demographics, and ICD-10 HCC mappings.
ICD-10 to HCC Mapping Engine
Maps ICD-10 codes to related HCC categories, with logic checks for non-specific diagnosis entries, age mismatches, and deleted codes included.
Enforcement of HCC Coding Guidelines
Ensures adherence to CMS and HCC coding guidelines, preventing overcoding or omitting HCC categories.
Validation of Z Code and MEAT Criteria
To ensure full documentation for RAF tool calculations, Captures Z codes and validates MEAT (Monitor, Evaluate, Assess/Address, and Treat) elements.
How Cogent AI Functions in HCC Medical Coding Automation
By enabling automated chart review, NLP-driven diagnosis mining, and HCC risk score optimization—all built for value-based care—you can improve the accuracy of HCC risk adjustment coding.
The following are functions of Cogent AI: Smart HCC Coding Software;
MEAT Criteria Validation in HCC Medical Coding
Cogent AI verifies each diagnosis against the MEAT criteria (Monitor, Evaluate, Assess, and Treat) to ensure compliance to Medicare risk adjustment coding. This eliminates denials, increases the accuracy of RAF scores, and matches with HCC coding guidelines by providing how every ICD-10 HCC code and Z code submitted for HCC medical coding is fully supported by documentation.
Monitor
Documentation of signs, symptoms, disease progression, regression, and ongoing monitoring is captured by Cogent AI. This ensures that all chronic conditions under observation are linked with exact HCC diagnosis codes and are represented in the RAF tool.
Evaluation
Automatically analyzes test results, physical examination findings, lab results, and treatment responses. Risk adjustment compliance is increased by AI-driven analysis, which confirms that each ICD-10 HCC mapping complies with HCC coding standards.
Assessment
Identifies counseling notes, record reviews, Doctor Conversation and care plans to validate assessment. Cogent AI links data to the right HCC codes list and ensures accurate risk adjustment factor mapping.
Treatment
Documents care plans including medication, referrals, diagnostic studies, and therapies. Cogent AI ensures that every treatment aligns with HCC Medicare requirements, optimizing RAF score calculation and strengthening HCC risk adjustment.
Overcoming Real-World Challenges in HCC Medical Coding Automation
Incomplete RAF Score Documentation
Cogent AI addresses gaps in RAF tool usage and documentation for payers by automatically capturing and scoring relevant risk adjustment factors for accurate HCC risk score submission.
Confusion in ICD-10 HCC assignment
Errors in ICD-10 HCC mapping lead to missed revenue. Cogent AI fixs this with smart suggestions and audit-ready HCC diagnosis codes.
Variability in Urgent Care Medical Coding
HCC coding is challenging in urgent care due to a lack of documentation. Cogent AI fills in the gaps by identifying chronic risk factors and mining brief texts.
Lack of Consistency in HCC Coding Guidelines
Inconsistency arises from manual interpretation of changing HCC coding guidelines. With CMS-aligned rules, Cogent AI maintains code updates.
Risk Adjustment Factor Misalignment
Reimbursement is reduced when the risk adjustment factor is incorrectly recorded. Cogent AI ensures accurate mapping to each patient’s RAF.
Missing HCC Medical Acronym Tagging
Failure to detect abbreviations like “CKD” “COPD” affects HCC identifier tagging. For complete code coverage, Cogent AI understands medical shorthand.
Delayed Risk Adjustment Submission
Workflows are automated by Cogent AI, which integrates with payer platforms to submit Medicare risk adjustment codes in real time.
Unknown HCC risk factors
Coders may overlook risk-driving conditions. Cogent AI highlights chronic patterns in longitudinal patient data.
RAF Score Calculator Inaccuracy
Manually calculating RAF scores leads to human error. Using the HHS risk model, Cogent AI automates scoring.
High-Volume HCC Coding Burnout
Routine HCC coding causes fatigue. Automation ensures coder efficiency without compromising quality.
To know Why Choose Cogent AI at ArtigenTech, Here is our real time result
| Results | How Cogent AI delivered |
| 96% RAF Accuracy | Assigns RAF by Z code detection and MEAT-validated ICD-10 HCC codes |
| 40% Faster Coding | Reduces chart review by 80% and automates HCC extraction. |
| 35% Fewer Errors | Ensures each HCC diagnosis code complies with CMS regulations. |
| 100+ HCC Categories | Tracks conditions from the full HCC codes list |
| 250+ Hours Saved | Uses actual time automation to automate HCC medical coding workflows. |
| CMS-Ready Output | Provides payer-compliant, structured files for Medicare risk adjustment coding. |
| 22% Revenue Increase | Higher payouts result from cleaner submissions and fewer rejections. |
| 100% Z Code Coverage | Finds every Z code that applies and is connected to HCC coding guidelines. |
| MEAT Criteria Verified | Verifies HCC records against the risk adjustment factor and MEAT regulations. |
| Full Audit Logs | All interactions with HCC coders are audit-ready and traceable. |
Watch How medical coding automation in Cogent AI Simplifies HCC Coding
Discover how Cogent AI detects Z codes, calculates accurate RAF scores, validates MEAT criteria, automates HCC medical coding, and matches ICD-10 HCC codes with Medicare risk adjustment guidelines. Use Cogent AI to automate risk adjustment processes, enhance coding precision, and increase reimbursements.
Request free demo today and see how AI can revolutionize your HCC coding workflow.
Get started with Cogent AI today and experience the benefits of advanced medical coding automation in your HCC practice today.
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USA Address
614 N. Dupont HWY, STE 210 Dover, DE – 19901
Indian Address
No 110, Uthamar Gandhi Rd, Subba Road Avenue, Nungambakkam
Call or Text
+1 213-291-7810
Email Us Today
contactus@artigentech.com
